Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations154
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.7 KiB
Average record size in memory164.0 B

Variable types

Numeric18
Boolean8
Categorical1

Alerts

Cluster is highly overall correlated with clorofila_a_ug_l and 4 other fieldsHigh correlation
campaña_Verano is highly overall correlated with tem_agua and 1 other fieldsHigh correlation
campaña_invierno is highly overall correlated with clorofila_a_ug_l and 2 other fieldsHigh correlation
campaña_otoño is highly overall correlated with dqo_mg_l and 1 other fieldsHigh correlation
clorofila_a_ug_l is highly overall correlated with Cluster and 1 other fieldsHigh correlation
colif_fecales_ufc_100ml is highly overall correlated with Cluster and 1 other fieldsHigh correlation
dbo_mg_l is highly overall correlated with ClusterHigh correlation
dqo_mg_l is highly overall correlated with campaña_otoñoHigh correlation
enteroc_ufc_100ml is highly overall correlated with espumasHigh correlation
espumas is highly overall correlated with enteroc_ufc_100mlHigh correlation
fosf_ortofos_mg_l is highly overall correlated with Cluster and 2 other fieldsHigh correlation
ica is highly overall correlated with colif_fecales_ufc_100ml and 3 other fieldsHigh correlation
microcistina_ug_l is highly overall correlated with tem_agua and 1 other fieldsHigh correlation
nitrato_mg_l is highly overall correlated with campaña_inviernoHigh correlation
od is highly overall correlated with phHigh correlation
olores is highly overall correlated with icaHigh correlation
p_total_l_mg_l is highly overall correlated with Cluster and 2 other fieldsHigh correlation
ph is highly overall correlated with odHigh correlation
tem_agua is highly overall correlated with campaña_Verano and 3 other fieldsHigh correlation
tem_aire is highly overall correlated with campaña_Verano and 2 other fieldsHigh correlation
turbiedad_ntu is highly overall correlated with campaña_otoñoHigh correlation
olores is highly imbalanced (60.5%) Imbalance
color is highly imbalanced (58.2%) Imbalance
espumas is highly imbalanced (79.3%) Imbalance
Cluster is highly imbalanced (75.6%) Imbalance
tem_aire has 7 (4.5%) zeros Zeros
enteroc_ufc_100ml has 6 (3.9%) zeros Zeros
fosf_ortofos_mg_l has 3 (1.9%) zeros Zeros
dbo_mg_l has 11 (7.1%) zeros Zeros
dqo_mg_l has 54 (35.1%) zeros Zeros
cr_total_mg_l has 117 (76.0%) zeros Zeros
clorofila_a_ug_l has 74 (48.1%) zeros Zeros
microcistina_ug_l has 109 (70.8%) zeros Zeros
ica has 10 (6.5%) zeros Zeros

Reproduction

Analysis started2024-11-03 19:26:16.504540
Analysis finished2024-11-03 19:28:24.913298
Duration2 minutes and 8.41 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

tem_agua
Real number (ℝ)

High correlation 

Distinct100
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.024162338
Minimum-2.241
Maximum1.746
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)50.0%
Memory size2.4 KiB
2024-11-03T16:28:25.306398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-2.241
5-th percentile-1.496
Q1-0.5585
median0
Q30.442
95-th percentile1.3926
Maximum1.746
Range3.987
Interquartile range (IQR)1.0005

Descriptive statistics

Standard deviation0.88205733
Coefficient of variation (CV)-36.505463
Kurtosis-0.33114396
Mean-0.024162338
Median Absolute Deviation (MAD)0.5495
Skewness-0.12532474
Sum-3.721
Variance0.77802513
MonotonicityNot monotonic
2024-11-03T16:28:25.942232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.496 7
 
4.5%
0.367 6
 
3.9%
0.088 5
 
3.2%
0.106 5
 
3.2%
-0.192 4
 
2.6%
0.926 3
 
1.9%
0.032 3
 
1.9%
1.243 3
 
1.9%
-0.453 3
 
1.9%
-0.173 3
 
1.9%
Other values (90) 112
72.7%
ValueCountFrequency (%)
-2.241 1
 
0.6%
-2.055 2
 
1.3%
-1.869 2
 
1.3%
-1.682 1
 
0.6%
-1.496 7
4.5%
-1.494 1
 
0.6%
-1.31 1
 
0.6%
-1.308 1
 
0.6%
-1.123 1
 
0.6%
-0.993 2
 
1.3%
ValueCountFrequency (%)
1.746 1
0.6%
1.671 1
0.6%
1.541 1
0.6%
1.503 2
1.3%
1.485 1
0.6%
1.448 1
0.6%
1.429 1
0.6%
1.373 2
1.3%
1.336 1
0.6%
1.317 1
0.6%

tem_aire
Real number (ℝ)

High correlation  Zeros 

Distinct36
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15171429
Minimum-2.2
Maximum2.4
Zeros7
Zeros (%)4.5%
Negative76
Negative (%)49.4%
Memory size2.4 KiB
2024-11-03T16:28:26.450159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-2.2
5-th percentile-1.4
Q1-0.4
median0
Q30.6
95-th percentile2.04
Maximum2.4
Range4.6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.97852391
Coefficient of variation (CV)6.4497809
Kurtosis-0.082644355
Mean0.15171429
Median Absolute Deviation (MAD)0.4
Skewness0.45626231
Sum23.364
Variance0.95750904
MonotonicityNot monotonic
2024-11-03T16:28:27.047586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
-0.2 25
16.2%
-0.4 15
 
9.7%
0.2 13
 
8.4%
-0.6 12
 
7.8%
0.4 9
 
5.8%
0 7
 
4.5%
1.4 6
 
3.9%
1.66 5
 
3.2%
-1.4 5
 
3.2%
-1 5
 
3.2%
Other values (26) 52
33.8%
ValueCountFrequency (%)
-2.2 1
 
0.6%
-2 1
 
0.6%
-1.8 1
 
0.6%
-1.6 1
 
0.6%
-1.4 5
3.2%
-1.2 3
 
1.9%
-1 5
3.2%
-0.8 4
 
2.6%
-0.6 12
7.8%
-0.54 1
 
0.6%
ValueCountFrequency (%)
2.4 5
3.2%
2.2 1
 
0.6%
2.04 3
1.9%
2 1
 
0.6%
1.66 5
3.2%
1.6 5
3.2%
1.44 2
 
1.3%
1.4 6
3.9%
1.2 2
 
1.3%
1 4
2.6%

od
Real number (ℝ)

High correlation 

Distinct149
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00038961039
Minimum-2.109
Maximum3.578
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)50.0%
Memory size2.4 KiB
2024-11-03T16:28:27.427827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-2.109
5-th percentile-1.5016
Q1-0.47525
median0
Q30.52475
95-th percentile1.3486
Maximum3.578
Range5.687
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.87679762
Coefficient of variation (CV)2250.4472
Kurtosis1.2621271
Mean0.00038961039
Median Absolute Deviation (MAD)0.5045
Skewness0.21749126
Sum0.06
Variance0.76877407
MonotonicityNot monotonic
2024-11-03T16:28:27.876288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.461 2
 
1.3%
0.08 2
 
1.3%
0.74 2
 
1.3%
-0.817 2
 
1.3%
0.36 2
 
1.3%
-1.074 1
 
0.6%
-1.733 1
 
0.6%
-0.151 1
 
0.6%
-0.747 1
 
0.6%
-0.958 1
 
0.6%
Other values (139) 139
90.3%
ValueCountFrequency (%)
-2.109 1
0.6%
-2.079 1
0.6%
-1.892 1
0.6%
-1.855 1
0.6%
-1.77 1
0.6%
-1.733 1
0.6%
-1.634 1
0.6%
-1.512 1
0.6%
-1.496 1
0.6%
-1.486 1
0.6%
ValueCountFrequency (%)
3.578 1
0.6%
2.006 1
0.6%
1.778 1
0.6%
1.729 1
0.6%
1.669 1
0.6%
1.415 1
0.6%
1.406 1
0.6%
1.359 1
0.6%
1.343 1
0.6%
1.267 1
0.6%

ph
Real number (ℝ)

High correlation 

Distinct114
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.061116883
Minimum-3.006
Maximum2.935
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)50.0%
Memory size2.4 KiB
2024-11-03T16:28:28.452300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-3.006
5-th percentile-1.16
Q1-0.46725
median0
Q30.533
95-th percentile1.5114
Maximum2.935
Range5.941
Interquartile range (IQR)1.00025

Descriptive statistics

Standard deviation0.81203748
Coefficient of variation (CV)13.286631
Kurtosis2.3338254
Mean0.061116883
Median Absolute Deviation (MAD)0.4825
Skewness0.41910283
Sum9.412
Variance0.65940488
MonotonicityNot monotonic
2024-11-03T16:28:28.980322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.166 7
 
4.5%
0.047 4
 
2.6%
0.071 4
 
2.6%
-0.047 3
 
1.9%
-0.284 3
 
1.9%
0.26 3
 
1.9%
0.154 3
 
1.9%
0.568 3
 
1.9%
0.308 3
 
1.9%
0.533 3
 
1.9%
Other values (104) 118
76.6%
ValueCountFrequency (%)
-3.006 1
0.6%
-1.586 1
0.6%
-1.385 1
0.6%
-1.361 1
0.6%
-1.254 1
0.6%
-1.195 1
0.6%
-1.183 1
0.6%
-1.16 2
1.3%
-1.124 1
0.6%
-1.041 1
0.6%
ValueCountFrequency (%)
2.935 1
0.6%
2.888 1
0.6%
2.189 1
0.6%
1.929 1
0.6%
1.917 1
0.6%
1.74 1
0.6%
1.669 1
0.6%
1.527 1
0.6%
1.503 1
0.6%
1.278 1
0.6%

olores
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
142 
True
 
12
ValueCountFrequency (%)
False 142
92.2%
True 12
 
7.8%
2024-11-03T16:28:29.580811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

color
Boolean

Imbalance 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
141 
True
 
13
ValueCountFrequency (%)
False 141
91.6%
True 13
 
8.4%
2024-11-03T16:28:29.867421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

espumas
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
149 
True
 
5
ValueCountFrequency (%)
False 149
96.8%
True 5
 
3.2%
2024-11-03T16:28:30.176974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

mat_susp
Boolean

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
127 
True
27 
ValueCountFrequency (%)
False 127
82.5%
True 27
 
17.5%
2024-11-03T16:28:30.489675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

colif_fecales_ufc_100ml
Real number (ℝ)

High correlation 

Distinct83
Distinct (%)53.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2653896
Minimum-0.105
Maximum115.595
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)50.0%
Memory size2.4 KiB
2024-11-03T16:28:30.879902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.105
5-th percentile-0.10005
Q1-0.074
median0
Q30.92525
95-th percentile7.42725
Maximum115.595
Range115.7
Interquartile range (IQR)0.99925

Descriptive statistics

Standard deviation10.470686
Coefficient of variation (CV)4.6220245
Kurtosis92.357265
Mean2.2653896
Median Absolute Deviation (MAD)0.0915
Skewness8.9943862
Sum348.87
Variance109.63527
MonotonicityNot monotonic
2024-11-03T16:28:31.384566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.444 6
 
3.9%
-0.08 5
 
3.2%
-0.058 5
 
3.2%
-0.069 5
 
3.2%
-0.072 4
 
2.6%
-0.025 4
 
2.6%
-0.083 4
 
2.6%
0.994 4
 
2.6%
0.058 3
 
1.9%
-0.074 3
 
1.9%
Other values (73) 111
72.1%
ValueCountFrequency (%)
-0.105 2
1.3%
-0.104 2
1.3%
-0.103 2
1.3%
-0.102 2
1.3%
-0.099 2
1.3%
-0.098 1
 
0.6%
-0.096 3
1.9%
-0.094 1
 
0.6%
-0.092 1
 
0.6%
-0.091 3
1.9%
ValueCountFrequency (%)
115.595 1
0.6%
43.97 1
0.6%
29.369 1
0.6%
20.278 1
0.6%
19.176 1
0.6%
11.463 1
0.6%
10.912 1
0.6%
7.606 1
0.6%
7.331 1
0.6%
6.504 1
0.6%

escher_coli_ufc_100ml
Real number (ℝ)

Distinct77
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3622662
Minimum-0.222
Maximum95.016
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)50.0%
Memory size2.4 KiB
2024-11-03T16:28:31.878842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.222
5-th percentile-0.21835
Q1-0.159
median0
Q30.84125
95-th percentile7.9304
Maximum95.016
Range95.238
Interquartile range (IQR)1.00025

Descriptive statistics

Standard deviation9.531084
Coefficient of variation (CV)4.0347205
Kurtosis63.204771
Mean2.3622662
Median Absolute Deviation (MAD)0.2105
Skewness7.3356541
Sum363.789
Variance90.841563
MonotonicityNot monotonic
2024-11-03T16:28:32.425981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.159 17
 
11.0%
-0.095 15
 
9.7%
-0.032 6
 
3.9%
0.159 5
 
3.2%
0.095 4
 
2.6%
-0.218 4
 
2.6%
-0.22 4
 
2.6%
0.413 4
 
2.6%
6.127 4
 
2.6%
1.048 3
 
1.9%
Other values (67) 88
57.1%
ValueCountFrequency (%)
-0.222 1
 
0.6%
-0.221 2
1.3%
-0.22 4
2.6%
-0.219 1
 
0.6%
-0.218 4
2.6%
-0.217 1
 
0.6%
-0.214 1
 
0.6%
-0.213 1
 
0.6%
-0.212 1
 
0.6%
-0.211 1
 
0.6%
ValueCountFrequency (%)
95.016 1
0.6%
50.571 1
0.6%
31.524 1
0.6%
27.714 1
0.6%
22 1
0.6%
17.556 1
0.6%
9.302 1
0.6%
8.921 1
0.6%
7.397 1
0.6%
7.079 1
0.6%

enteroc_ufc_100ml
Real number (ℝ)

High correlation  Zeros 

Distinct85
Distinct (%)55.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0457403
Minimum-0.483
Maximum44.858
Zeros6
Zeros (%)3.9%
Negative75
Negative (%)48.7%
Memory size2.4 KiB
2024-11-03T16:28:32.967621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.483
5-th percentile-0.478
Q1-0.405
median0
Q30.595
95-th percentile4.01625
Maximum44.858
Range45.341
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.8575433
Coefficient of variation (CV)4.6450763
Kurtosis54.448373
Mean1.0457403
Median Absolute Deviation (MAD)0.437
Skewness6.980367
Sum161.044
Variance23.595727
MonotonicityNot monotonic
2024-11-03T16:28:33.427226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.324 7
 
4.5%
-0.47 6
 
3.9%
0 6
 
3.9%
-0.405 6
 
3.9%
-0.453 5
 
3.2%
1.943 5
 
3.2%
-0.483 5
 
3.2%
-0.437 4
 
2.6%
1.296 3
 
1.9%
-0.356 3
 
1.9%
Other values (75) 104
67.5%
ValueCountFrequency (%)
-0.483 5
3.2%
-0.481 1
 
0.6%
-0.479 1
 
0.6%
-0.478 2
 
1.3%
-0.471 1
 
0.6%
-0.47 6
3.9%
-0.468 1
 
0.6%
-0.453 5
3.2%
-0.447 1
 
0.6%
-0.442 1
 
0.6%
ValueCountFrequency (%)
44.858 1
0.6%
31.903 1
0.6%
18.947 1
0.6%
11.66 1
0.6%
7.611 1
0.6%
6.316 1
0.6%
5.992 1
0.6%
4.858 1
0.6%
3.563 1
0.6%
3.077 1
0.6%

nitrato_mg_l
Real number (ℝ)

High correlation 

Distinct84
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20768182
Minimum-0.739
Maximum3.202
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)50.0%
Memory size2.4 KiB
2024-11-03T16:28:33.913544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.739
5-th percentile-0.719
Q1-0.404
median0
Q30.596
95-th percentile1.56065
Maximum3.202
Range3.941
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79958039
Coefficient of variation (CV)3.8500259
Kurtosis1.0478327
Mean0.20768182
Median Absolute Deviation (MAD)0.4925
Skewness1.1217706
Sum31.983
Variance0.63932879
MonotonicityNot monotonic
2024-11-03T16:28:34.565821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.719 6
 
3.9%
-0.463 5
 
3.2%
-0.739 4
 
2.6%
-0.108 4
 
2.6%
-0.345 4
 
2.6%
-0.384 4
 
2.6%
0.049 4
 
2.6%
-0.542 3
 
1.9%
-0.089 3
 
1.9%
-0.01 3
 
1.9%
Other values (74) 114
74.0%
ValueCountFrequency (%)
-0.739 4
2.6%
-0.719 6
3.9%
-0.7 2
 
1.3%
-0.68 1
 
0.6%
-0.64 1
 
0.6%
-0.621 1
 
0.6%
-0.601 3
1.9%
-0.581 3
1.9%
-0.562 2
 
1.3%
-0.542 3
1.9%
ValueCountFrequency (%)
3.202 1
0.6%
2.946 1
0.6%
2.118 1
0.6%
2.099 1
0.6%
2.079 1
0.6%
1.803 1
0.6%
1.724 1
0.6%
1.586 1
0.6%
1.547 1
0.6%
1.507 2
1.3%

nh4_mg_l
Real number (ℝ)

Distinct83
Distinct (%)53.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.82104545
Minimum-0.382
Maximum14.213
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)50.0%
Memory size2.4 KiB
2024-11-03T16:28:35.213621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.382
5-th percentile-0.382
Q1-0.34825
median0
Q30.652
95-th percentile4.45445
Maximum14.213
Range14.595
Interquartile range (IQR)1.00025

Descriptive statistics

Standard deviation2.6199042
Coefficient of variation (CV)3.1909369
Kurtosis16.287198
Mean0.82104545
Median Absolute Deviation (MAD)0.375
Skewness3.9375375
Sum126.441
Variance6.8638982
MonotonicityNot monotonic
2024-11-03T16:28:35.867510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.382 30
 
19.5%
0.859 5
 
3.2%
-0.35 5
 
3.2%
-0.153 5
 
3.2%
0.223 4
 
2.6%
0.795 3
 
1.9%
-0.127 3
 
1.9%
0.413 3
 
1.9%
0.064 2
 
1.3%
0.095 2
 
1.3%
Other values (73) 92
59.7%
ValueCountFrequency (%)
-0.382 30
19.5%
-0.375 2
 
1.3%
-0.362 2
 
1.3%
-0.35 5
 
3.2%
-0.343 2
 
1.3%
-0.337 1
 
0.6%
-0.324 1
 
0.6%
-0.318 1
 
0.6%
-0.305 1
 
0.6%
-0.299 2
 
1.3%
ValueCountFrequency (%)
14.213 2
1.3%
13.577 2
1.3%
11.669 1
0.6%
7.218 1
0.6%
5.501 1
0.6%
4.992 1
0.6%
4.165 1
0.6%
4.102 1
0.6%
3.211 1
0.6%
3.148 1
0.6%

p_total_l_mg_l
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7031818
Minimum-0.927
Maximum96.699
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)50.0%
Memory size2.4 KiB
2024-11-03T16:28:36.303541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.927
5-th percentile-0.8082
Q1-0.39875
median0
Q30.602
95-th percentile3.08975
Maximum96.699
Range97.626
Interquartile range (IQR)1.00075

Descriptive statistics

Standard deviation11.108774
Coefficient of variation (CV)6.5223653
Kurtosis69.688959
Mean1.7031818
Median Absolute Deviation (MAD)0.472
Skewness8.3081581
Sum262.29
Variance123.40486
MonotonicityNot monotonic
2024-11-03T16:28:36.749826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.179 8
 
5.2%
-0.504 6
 
3.9%
-0.472 5
 
3.2%
-0.341 4
 
2.6%
-0.374 4
 
2.6%
-0.927 4
 
2.6%
0.049 4
 
2.6%
2.65 4
 
2.6%
-0.081 4
 
2.6%
0.602 3
 
1.9%
Other values (67) 108
70.1%
ValueCountFrequency (%)
-0.927 4
2.6%
-0.894 1
 
0.6%
-0.862 1
 
0.6%
-0.829 2
1.3%
-0.797 1
 
0.6%
-0.764 1
 
0.6%
-0.699 2
1.3%
-0.667 2
1.3%
-0.634 3
1.9%
-0.602 1
 
0.6%
ValueCountFrequency (%)
96.699 2
1.3%
19.899 1
 
0.6%
7.854 1
 
0.6%
4.927 1
 
0.6%
3.626 1
 
0.6%
3.301 2
1.3%
2.976 2
1.3%
2.65 4
2.6%
2.325 1
 
0.6%
2.195 1
 
0.6%

fosf_ortofos_mg_l
Real number (ℝ)

High correlation  Zeros 

Distinct62
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31944805
Minimum-0.86
Maximum8.486
Zeros3
Zeros (%)1.9%
Negative75
Negative (%)48.7%
Memory size2.4 KiB
2024-11-03T16:28:37.176414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.86
5-th percentile-0.7233
Q1-0.374
median0
Q30.6265
95-th percentile2.2205
Maximum8.486
Range9.346
Interquartile range (IQR)1.0005

Descriptive statistics

Standard deviation1.1539417
Coefficient of variation (CV)3.6122983
Kurtosis16.93349
Mean0.31944805
Median Absolute Deviation (MAD)0.43
Skewness3.2484274
Sum49.195
Variance1.3315813
MonotonicityNot monotonic
2024-11-03T16:28:37.715250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.037 7
 
4.5%
-0.486 7
 
4.5%
-0.224 7
 
4.5%
0.785 6
 
3.9%
-0.075 6
 
3.9%
-0.561 5
 
3.2%
0.224 5
 
3.2%
0.262 5
 
3.2%
0.075 4
 
2.6%
-0.336 4
 
2.6%
Other values (52) 98
63.6%
ValueCountFrequency (%)
-0.86 4
2.6%
-0.822 2
 
1.3%
-0.785 1
 
0.6%
-0.748 1
 
0.6%
-0.71 1
 
0.6%
-0.673 3
1.9%
-0.636 2
 
1.3%
-0.598 2
 
1.3%
-0.561 5
3.2%
-0.523 3
1.9%
ValueCountFrequency (%)
8.486 1
0.6%
4 2
1.3%
3.626 1
0.6%
3.252 2
1.3%
2.505 1
0.6%
2.318 1
0.6%
2.168 1
0.6%
2.056 1
0.6%
1.981 2
1.3%
1.944 2
1.3%

dbo_mg_l
Real number (ℝ)

High correlation  Zeros 

Distinct71
Distinct (%)46.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18544156
Minimum-0.722
Maximum8.615
Zeros11
Zeros (%)7.1%
Negative73
Negative (%)47.4%
Memory size2.4 KiB
2024-11-03T16:28:38.230776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.722
5-th percentile-0.722
Q1-0.517
median0
Q30.48325
95-th percentile2.095
Maximum8.615
Range9.337
Interquartile range (IQR)1.00025

Descriptive statistics

Standard deviation1.0898607
Coefficient of variation (CV)5.8771115
Kurtosis23.372552
Mean0.18544156
Median Absolute Deviation (MAD)0.517
Skewness3.7381599
Sum28.558
Variance1.1877964
MonotonicityNot monotonic
2024-11-03T16:28:38.814794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.698 16
 
10.4%
0 11
 
7.1%
-0.722 10
 
6.5%
-0.517 10
 
6.5%
1.63 5
 
3.2%
-0.233 4
 
2.6%
0.056 4
 
2.6%
0.349 4
 
2.6%
-0.512 3
 
1.9%
2.095 3
 
1.9%
Other values (61) 84
54.5%
ValueCountFrequency (%)
-0.722 10
6.5%
-0.698 16
10.4%
-0.661 1
 
0.6%
-0.629 1
 
0.6%
-0.605 2
 
1.3%
-0.582 2
 
1.3%
-0.559 1
 
0.6%
-0.517 10
6.5%
-0.512 3
 
1.9%
-0.475 1
 
0.6%
ValueCountFrequency (%)
8.615 1
 
0.6%
3.725 1
 
0.6%
3.027 2
 
1.3%
2.561 1
 
0.6%
2.328 1
 
0.6%
2.095 3
1.9%
1.63 5
3.2%
1.397 3
1.9%
1.164 2
 
1.3%
1.024 3
1.9%

dqo_mg_l
Real number (ℝ)

High correlation  Zeros 

Distinct48
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63767532
Minimum-0.045
Maximum6.742
Zeros54
Zeros (%)35.1%
Negative27
Negative (%)17.5%
Memory size2.4 KiB
2024-11-03T16:28:39.278975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.045
5-th percentile-0.045
Q10
median0
Q31.00025
95-th percentile2.49
Maximum6.742
Range6.787
Interquartile range (IQR)1.00025

Descriptive statistics

Standard deviation1.0551679
Coefficient of variation (CV)1.6547102
Kurtosis8.5824046
Mean0.63767532
Median Absolute Deviation (MAD)0.045
Skewness2.47785
Sum98.202
Variance1.1133793
MonotonicityNot monotonic
2024-11-03T16:28:39.727835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 54
35.1%
-0.045 27
17.5%
0.899 7
 
4.5%
0.404 4
 
2.6%
0.135 3
 
1.9%
0.27 3
 
1.9%
0.809 3
 
1.9%
1.303 2
 
1.3%
1.483 2
 
1.3%
2.247 2
 
1.3%
Other values (38) 47
30.5%
ValueCountFrequency (%)
-0.045 27
17.5%
0 54
35.1%
0.045 1
 
0.6%
0.09 2
 
1.3%
0.135 3
 
1.9%
0.18 2
 
1.3%
0.225 1
 
0.6%
0.27 3
 
1.9%
0.315 1
 
0.6%
0.404 4
 
2.6%
ValueCountFrequency (%)
6.742 1
0.6%
4.719 1
0.6%
4.494 1
0.6%
3.596 1
0.6%
2.876 1
0.6%
2.697 1
0.6%
2.652 1
0.6%
2.607 1
0.6%
2.427 1
0.6%
2.337 2
1.3%

turbiedad_ntu
Real number (ℝ)

High correlation 

Distinct60
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24437013
Minimum-0.893
Maximum3.661
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)50.0%
Memory size2.4 KiB
2024-11-03T16:28:40.349595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.893
5-th percentile-0.69125
Q1-0.375
median0
Q30.625
95-th percentile2.054
Maximum3.661
Range4.554
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.86479331
Coefficient of variation (CV)3.5388667
Kurtosis1.4433274
Mean0.24437013
Median Absolute Deviation (MAD)0.411
Skewness1.3035612
Sum37.633
Variance0.74786748
MonotonicityNot monotonic
2024-11-03T16:28:41.115909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.554 7
 
4.5%
-0.304 6
 
3.9%
0.625 6
 
3.9%
-0.196 6
 
3.9%
0.018 5
 
3.2%
-0.161 5
 
3.2%
2.232 5
 
3.2%
1.161 5
 
3.2%
-0.518 5
 
3.2%
-0.054 5
 
3.2%
Other values (50) 99
64.3%
ValueCountFrequency (%)
-0.893 1
0.6%
-0.864 1
0.6%
-0.836 1
0.6%
-0.768 1
0.6%
-0.764 1
0.6%
-0.721 1
0.6%
-0.714 2
1.3%
-0.679 1
0.6%
-0.675 1
0.6%
-0.664 1
0.6%
ValueCountFrequency (%)
3.661 1
 
0.6%
2.946 1
 
0.6%
2.232 5
3.2%
2.054 2
 
1.3%
2.018 1
 
0.6%
1.875 2
 
1.3%
1.696 3
1.9%
1.554 1
 
0.6%
1.518 3
1.9%
1.411 1
 
0.6%

cr_total_mg_l
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5390974
Minimum0
Maximum11.995
Zeros117
Zeros (%)76.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-03T16:28:41.942452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.995
Maximum11.995
Range11.995
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9460273
Coefficient of variation (CV)3.609788
Kurtosis14.250176
Mean0.5390974
Median Absolute Deviation (MAD)0
Skewness3.7688678
Sum83.021
Variance3.7870223
MonotonicityNot monotonic
2024-11-03T16:28:42.647535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 117
76.0%
0.001 8
 
5.2%
0.002 7
 
4.5%
5.995 4
 
2.6%
0.003 3
 
1.9%
4.995 3
 
1.9%
0.004 2
 
1.3%
0.006 2
 
1.3%
6.995 2
 
1.3%
0.01 1
 
0.6%
Other values (5) 5
 
3.2%
ValueCountFrequency (%)
0 117
76.0%
0.001 8
 
5.2%
0.002 7
 
4.5%
0.003 3
 
1.9%
0.004 2
 
1.3%
0.005 1
 
0.6%
0.006 2
 
1.3%
0.01 1
 
0.6%
0.015 1
 
0.6%
4.995 3
 
1.9%
ValueCountFrequency (%)
11.995 1
 
0.6%
9.995 1
 
0.6%
7.995 1
 
0.6%
6.995 2
1.3%
5.995 4
2.6%
4.995 3
1.9%
0.015 1
 
0.6%
0.01 1
 
0.6%
0.006 2
1.3%
0.005 1
 
0.6%

clorofila_a_ug_l
Real number (ℝ)

High correlation  Zeros 

Distinct72
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1923312
Minimum-0.235
Maximum150.323
Zeros74
Zeros (%)48.1%
Negative24
Negative (%)15.6%
Memory size2.4 KiB
2024-11-03T16:28:43.166560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.235
5-th percentile-0.2287
Q10
median0
Q31.00025
95-th percentile35.36085
Maximum150.323
Range150.558
Interquartile range (IQR)1.00025

Descriptive statistics

Standard deviation19.273057
Coefficient of variation (CV)3.1124074
Kurtosis27.056059
Mean6.1923312
Median Absolute Deviation (MAD)0.041
Skewness4.7769453
Sum953.619
Variance371.45074
MonotonicityNot monotonic
2024-11-03T16:28:43.645886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74
48.1%
-0.235 5
 
3.2%
-0.228 3
 
1.9%
7.986 2
 
1.3%
-0.221 2
 
1.3%
-0.23 2
 
1.3%
0.466 1
 
0.6%
0.506 1
 
0.6%
2.823 1
 
0.6%
0.251 1
 
0.6%
Other values (62) 62
40.3%
ValueCountFrequency (%)
-0.235 5
3.2%
-0.233 1
 
0.6%
-0.23 2
 
1.3%
-0.228 3
1.9%
-0.225 1
 
0.6%
-0.223 1
 
0.6%
-0.221 2
 
1.3%
-0.218 1
 
0.6%
-0.216 1
 
0.6%
-0.211 1
 
0.6%
ValueCountFrequency (%)
150.323 1
0.6%
108.984 1
0.6%
84.087 1
0.6%
67.88 1
0.6%
58.485 1
0.6%
49.794 1
0.6%
45.802 1
0.6%
40.399 1
0.6%
32.648 1
0.6%
30.762 1
0.6%

microcistina_ug_l
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95405844
Minimum0
Maximum40.533
Zeros109
Zeros (%)70.8%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-03T16:28:44.283690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.00025
95-th percentile1.333
Maximum40.533
Range40.533
Interquartile range (IQR)1.00025

Descriptive statistics

Standard deviation4.1773601
Coefficient of variation (CV)4.3785159
Kurtosis61.670451
Mean0.95405844
Median Absolute Deviation (MAD)0
Skewness7.5070257
Sum146.925
Variance17.450337
MonotonicityNot monotonic
2024-11-03T16:28:44.910341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 109
70.8%
1.333 31
 
20.1%
0.267 4
 
2.6%
1.067 2
 
1.3%
22.667 2
 
1.3%
6.667 1
 
0.6%
0.533 1
 
0.6%
4 1
 
0.6%
40.533 1
 
0.6%
0.8 1
 
0.6%
ValueCountFrequency (%)
0 109
70.8%
0.267 4
 
2.6%
0.533 1
 
0.6%
0.8 1
 
0.6%
1.067 2
 
1.3%
1.333 31
 
20.1%
4 1
 
0.6%
4.533 1
 
0.6%
6.667 1
 
0.6%
22.667 2
 
1.3%
ValueCountFrequency (%)
40.533 1
 
0.6%
22.667 2
 
1.3%
6.667 1
 
0.6%
4.533 1
 
0.6%
4 1
 
0.6%
1.333 31
20.1%
1.067 2
 
1.3%
0.8 1
 
0.6%
0.533 1
 
0.6%
0.267 4
 
2.6%

ica
Real number (ℝ)

High correlation  Zeros 

Distinct38
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1725974
Minimum-1.583
Maximum2.833
Zeros10
Zeros (%)6.5%
Negative73
Negative (%)47.4%
Memory size2.4 KiB
2024-11-03T16:28:45.480070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1.583
5-th percentile-0.75
Q1-0.333
median0
Q30.667
95-th percentile1.44605
Maximum2.833
Range4.416
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7457027
Coefficient of variation (CV)4.3204746
Kurtosis0.32516167
Mean0.1725974
Median Absolute Deviation (MAD)0.417
Skewness0.65748332
Sum26.58
Variance0.55607252
MonotonicityNot monotonic
2024-11-03T16:28:46.139140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
-0.167 13
 
8.4%
0 10
 
6.5%
-0.417 10
 
6.5%
-0.5 9
 
5.8%
-0.333 8
 
5.2%
0.333 7
 
4.5%
-0.083 7
 
4.5%
-0.25 7
 
4.5%
0.25 7
 
4.5%
1.083 6
 
3.9%
Other values (28) 70
45.5%
ValueCountFrequency (%)
-1.583 1
 
0.6%
-1.417 1
 
0.6%
-1.083 2
 
1.3%
-0.917 1
 
0.6%
-0.833 2
 
1.3%
-0.75 4
 
2.6%
-0.667 3
 
1.9%
-0.583 5
3.2%
-0.5 9
5.8%
-0.417 10
6.5%
ValueCountFrequency (%)
2.833 1
 
0.6%
2.083 1
 
0.6%
1.833 1
 
0.6%
1.667 1
 
0.6%
1.583 2
 
1.3%
1.5 2
 
1.3%
1.417 5
3.2%
1.333 4
2.6%
1.25 1
 
0.6%
1.167 2
 
1.3%
Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
117 
True
37 
ValueCountFrequency (%)
False 117
76.0%
True 37
 
24.0%
2024-11-03T16:28:46.609662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

campaña_Verano
Boolean

High correlation 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
115 
True
39 
ValueCountFrequency (%)
False 115
74.7%
True 39
 
25.3%
2024-11-03T16:28:46.931000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

campaña_invierno
Boolean

High correlation 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
117 
True
37 
ValueCountFrequency (%)
False 117
76.0%
True 37
 
24.0%
2024-11-03T16:28:47.270485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

campaña_otoño
Boolean

High correlation 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
113 
True
41 
ValueCountFrequency (%)
False 113
73.4%
True 41
 
26.6%
2024-11-03T16:28:47.562504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Cluster
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
1
142 
3
 
8
0
 
3
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters154
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 142
92.2%
3 8
 
5.2%
0 3
 
1.9%
2 1
 
0.6%

Length

2024-11-03T16:28:48.011135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-03T16:28:48.464789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 142
92.2%
3 8
 
5.2%
0 3
 
1.9%
2 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 142
92.2%
3 8
 
5.2%
0 3
 
1.9%
2 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 154
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 142
92.2%
3 8
 
5.2%
0 3
 
1.9%
2 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 154
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 142
92.2%
3 8
 
5.2%
0 3
 
1.9%
2 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 154
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 142
92.2%
3 8
 
5.2%
0 3
 
1.9%
2 1
 
0.6%

Interactions

2024-11-03T16:28:15.494661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:21.315410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:27.789964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:33.702785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:40.110166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:46.479330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:53.026711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:59.694258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:05.470553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:11.593061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:19.032509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:25.927625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:32.397848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:38.464751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:46.374396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:54.915236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:01.584872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:08.260178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:15.827019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:21.687643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:28.192199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:34.016143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:40.533038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:46.799381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:53.432302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:00.103397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:05.835461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:12.063884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:19.682425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:26.238268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:32.756951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:38.923196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:46.711380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:55.278688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:01.968458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:08.667601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:16.125686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:21.990401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:28.451297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:34.263676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:40.800891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:47.118383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:53.731867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:00.388334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:06.164901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:12.360984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:19.997601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:26.479352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:33.095414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:39.203326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:46.980726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:55.639657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:02.452183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:08.990694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:16.422486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:22.287652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:28.766109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:34.549963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:41.058663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:47.443489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:54.081487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:00.648462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:06.519756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:12.672076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:20.432308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:26.793325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:33.514817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:39.529193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:47.262483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:56.147311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:02.765930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:09.347727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:16.860332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:22.580106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:29.043265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:34.865541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:41.326896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:47.888447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:54.389775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:00.946630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:06.930996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:12.999334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:20.828762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:27.102201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:33.792299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:40.064943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:47.616011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:56.604564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:03.059516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:09.759152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:17.175288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:22.878494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:29.324385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:35.229336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:41.682153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:48.242342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:54.804818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:01.260463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:07.260990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:13.279881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:21.148519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:27.496499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:34.092542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:40.486080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:48.053437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:56.977827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:03.389423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:10.199918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:17.490981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:23.302185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:29.551595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:35.618095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:42.043771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:48.550810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:55.165545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:01.548140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:07.556401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:13.580444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:21.464997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:27.868323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:34.389539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:40.823382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:48.563957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:57.238471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:03.793916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:10.533452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:17.848883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:23.609843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:30.088922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:35.947262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:42.312782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:48.888255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:55.479137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:01.827001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:07.863430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:13.914566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:21.765058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:28.220319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:34.656363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:41.154893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:49.058525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:57.621246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:04.126815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:10.888306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:18.137300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:23.948451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:30.476094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:36.266508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:42.955311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:49.247458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:55.824077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:02.225840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:08.190180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:14.258666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:22.122461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:28.717458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:35.025646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:41.572128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:49.614123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:57.989813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:04.578540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:11.276810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:19.293797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:24.345765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:30.777053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:36.593050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:43.333615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:49.656652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:56.187251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:02.588338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:08.572230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:14.630651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:22.436393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:29.295589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:35.358275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:42.118931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:50.215468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:58.286400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:04.918610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:11.716751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:19.656696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:24.691283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:31.048599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:36.944604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:43.725842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:49.982462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:56.487294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:02.908494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:08.890684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:15.218370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:22.871247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:29.673561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:35.688527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:42.625582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:50.551273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:58.779484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:05.228198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:12.134512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:19.978846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:24.994082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:31.325891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:37.347661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:44.058892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:50.291811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:56.809495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:03.185924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:09.154503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:15.550001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:23.154703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:30.037956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:36.050775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:43.004431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:50.819280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:59.090524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:05.519727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:12.452413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:20.223098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:25.339371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:31.610501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:37.624933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:44.350146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:50.592283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:57.146434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:03.509071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:09.458318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:15.894597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:23.434916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:30.295281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:36.430714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:43.299540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:51.330917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:59.358530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:05.812455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:12.850276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:20.670143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:25.790764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:31.960062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:38.105597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:44.677638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:51.010960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:57.469819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:03.868912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:09.821301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:16.422663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:23.912867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:30.652577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:36.787893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:43.906179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:52.354402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:59.717202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:06.144421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:13.290549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:21.020380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:26.231602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:32.258680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:38.399880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:44.930942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:51.294398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:57.744247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:04.136008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:10.110737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:16.828720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:24.264908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:30.957651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:37.105914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:44.245343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:53.123825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:59.989766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:06.628786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:13.684800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:21.270046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:26.628042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:32.769541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:38.826495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:45.346308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:51.687009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:58.107572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:04.430176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:10.460910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:17.247338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:24.644992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:31.253435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:37.444437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:45.172516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:53.663331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:00.511276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:07.130461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:14.125578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:21.613717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:26.954593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:33.070374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:39.259917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:45.846955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:52.138226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:58.378409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:04.756749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:10.887547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:17.728541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:25.066386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:31.633343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:37.807171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:45.572464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:54.241880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:00.918454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:07.523149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:14.612694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:21.961709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:27.493371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:33.412431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:39.742725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:46.178390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:52.576451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:26:59.325138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:05.102401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:11.254532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:18.168362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:25.523726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:32.090791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:38.145616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:46.097515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:27:54.652857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:01.270917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:07.962239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T16:28:15.054258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-03T16:28:48.889828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Clustercampaña_Primaveracampaña_Veranocampaña_inviernocampaña_otoñoclorofila_a_ug_lcolif_fecales_ufc_100mlcolorcr_total_mg_ldbo_mg_ldqo_mg_lenteroc_ufc_100mlescher_coli_ufc_100mlespumasfosf_ortofos_mg_licamat_suspmicrocistina_ug_lnh4_mg_lnitrato_mg_lodoloresp_total_l_mg_lphtem_aguatem_aireturbiedad_ntu
Cluster1.0000.1360.0950.4270.0570.5100.5600.2690.0000.5800.0000.4390.4390.1980.6260.0940.1250.0000.3560.0850.0950.2820.5620.0000.2130.0000.000
campaña_Primavera0.1361.0000.3000.2880.3120.0000.0000.0370.4270.1110.2270.0000.0170.0000.1040.2060.0000.0000.0000.2940.1630.1090.1750.0000.4710.4430.318
campaña_Verano0.0950.3001.0000.3000.3250.0000.0000.2120.0000.0000.2250.1020.0000.0000.0000.3310.0000.1610.0000.1790.0000.0000.0000.1500.8650.7640.149
campaña_invierno0.4270.2880.3001.0000.3120.5170.2840.0000.0000.2880.1920.0000.0000.0000.2610.4140.0000.0000.4930.5010.0130.0430.0000.1200.6970.3240.232
campaña_otoño0.0570.3120.3250.3121.0000.0000.0000.0000.0000.0000.7750.0000.2850.0000.0000.0210.0000.0880.0000.3150.1500.0000.0980.1470.4970.2750.528
clorofila_a_ug_l0.5100.0000.0000.5170.0001.0000.1060.000-0.0300.211-0.119-0.290-0.3660.0000.164-0.2360.0000.1960.0200.3690.1910.0000.1420.295-0.239-0.045-0.085
colif_fecales_ufc_100ml0.5600.0000.0000.2840.0000.1061.0000.0700.2150.3540.2160.2720.2250.2570.330-0.6040.131-0.2590.3750.230-0.3200.2930.367-0.170-0.3180.080-0.153
color0.2690.0370.2120.0000.0000.0000.0701.0000.0000.0000.0000.4310.3360.3990.2600.3630.2470.1070.3540.0000.2740.4730.1300.2590.3000.1810.000
cr_total_mg_l0.0000.4270.0000.0000.000-0.0300.2150.0001.0000.1920.201-0.0050.1140.0000.301-0.3520.000-0.2930.113-0.037-0.0010.0000.398-0.013-0.203-0.2270.166
dbo_mg_l0.5800.1110.0000.2880.0000.2110.3540.0000.1921.000-0.021-0.0120.0850.1940.449-0.3730.000-0.0530.3210.0720.0290.3720.4490.204-0.114-0.056-0.305
dqo_mg_l0.0000.2270.2250.1920.775-0.1190.2160.0000.201-0.0211.0000.1720.2560.0000.010-0.1830.000-0.372-0.0910.1830.0320.0000.1210.007-0.302-0.2460.331
enteroc_ufc_100ml0.4390.0000.1020.0000.000-0.2900.2720.431-0.005-0.0120.1721.0000.4780.602-0.043-0.1410.2890.0790.005-0.033-0.2660.3550.054-0.0940.1030.1600.032
escher_coli_ufc_100ml0.4390.0170.0000.0000.285-0.3660.2250.3360.1140.0850.2560.4781.0000.405-0.033-0.0660.208-0.1230.023-0.116-0.2400.2190.151-0.0920.130-0.0110.068
espumas0.1980.0000.0000.0000.0000.0000.2570.3990.0000.1940.0000.6020.4051.0000.3780.3380.2400.0000.4610.0000.0000.4190.0000.1670.2130.0000.000
fosf_ortofos_mg_l0.6260.1040.0000.2610.0000.1640.3300.2600.3010.4490.010-0.043-0.0330.3781.000-0.5030.0000.1580.3060.039-0.2140.4210.7760.137-0.0570.055-0.404
ica0.0940.2060.3310.4140.021-0.236-0.6040.363-0.352-0.373-0.183-0.141-0.0660.338-0.5031.0000.2700.210-0.493-0.1560.2560.560-0.575-0.0070.3340.0490.277
mat_susp0.1250.0000.0000.0000.0000.0000.1310.2470.0000.0000.0000.2890.2080.2400.0000.2701.0000.0000.1520.0000.2710.2690.0000.2140.3070.2540.000
microcistina_ug_l0.0000.0000.1610.0000.0880.196-0.2590.107-0.293-0.053-0.3720.079-0.1230.0000.1580.2100.0001.000-0.306-0.101-0.1080.0000.0400.0620.6040.555-0.079
nh4_mg_l0.3560.0000.0000.4930.0000.0200.3750.3540.1130.321-0.0910.0050.0230.4610.306-0.4930.152-0.3061.000-0.076-0.2680.3840.330-0.150-0.233-0.122-0.471
nitrato_mg_l0.0850.2940.1790.5010.3150.3690.2300.000-0.0370.0720.183-0.033-0.1160.0000.039-0.1560.000-0.101-0.0761.0000.1360.0000.0220.149-0.2420.039-0.029
od0.0950.1630.0000.0130.1500.191-0.3200.274-0.0010.0290.032-0.266-0.2400.000-0.2140.2560.271-0.108-0.2680.1361.0000.102-0.2330.587-0.224-0.1370.260
olores0.2820.1090.0000.0430.0000.0000.2930.4730.0000.3720.0000.3550.2190.4190.4210.5600.2690.0000.3840.0000.1021.0000.0000.2620.0000.0000.151
p_total_l_mg_l0.5620.1750.0000.0000.0980.1420.3670.1300.3980.4490.1210.0540.1510.0000.776-0.5750.0000.0400.3300.022-0.2330.0001.0000.107-0.073-0.011-0.303
ph0.0000.0000.1500.1200.1470.295-0.1700.259-0.0130.2040.007-0.094-0.0920.1670.137-0.0070.2140.062-0.1500.1490.5870.2620.1071.000-0.207-0.1990.017
tem_agua0.2130.4710.8650.6970.497-0.239-0.3180.300-0.203-0.114-0.3020.1030.1300.213-0.0570.3340.3070.604-0.233-0.242-0.2240.000-0.073-0.2071.0000.653-0.017
tem_aire0.0000.4430.7640.3240.275-0.0450.0800.181-0.227-0.056-0.2460.160-0.0110.0000.0550.0490.2540.555-0.1220.039-0.1370.000-0.011-0.1990.6531.000-0.061
turbiedad_ntu0.0000.3180.1490.2320.528-0.085-0.1530.0000.166-0.3050.3310.0320.0680.000-0.4040.2770.000-0.079-0.471-0.0290.2600.151-0.3030.017-0.017-0.0611.000

Missing values

2024-11-03T16:28:22.640341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-03T16:28:24.197373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

tem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntucr_total_mg_lclorofila_a_ug_lmicrocistina_ug_licacampaña_Primaveracampaña_Veranocampaña_inviernocampaña_otoñoCluster
01.2051.66-0.480-1.160FalseFalseFalseTrue-0.047-0.159-0.275-0.542-0.146-0.504-0.6730.279-0.0452.2320.00.01.3331.083FalseTrueFalseFalse1
11.3731.66-1.486-1.160TrueTrueFalseFalse-0.074-0.0950.162-0.463-0.0890.0810.0750.186-0.0450.2320.00.01.3330.000FalseTrueFalseFalse1
21.2241.66-1.258-1.124FalseTrueFalseFalse-0.058-0.0950.4530.167-0.3820.6670.785-0.722-0.045-0.3750.00.01.3330.250FalseTrueFalseFalse1
31.3361.66-1.496-0.107TrueTrueFalseFalse-0.069-0.1590.0000.3450.223-0.0160.2620.186-0.045-0.1610.00.01.3330.333FalseTrueFalseFalse1
41.1311.00-1.892-1.361FalseTrueFalseTrue-0.077-0.1590.1130.621-0.3820.5370.785-0.5591.303-0.3390.00.01.3330.167FalseTrueFalseFalse1
51.2801.66-1.074-1.195FalseFalseFalseTrue-0.019-0.0950.729-0.2461.8122.3252.168-0.2564.494-0.6640.00.01.333-0.167FalseTrueFalseFalse1
61.2051.00-1.733-1.183FalseTrueFalseTrue0.3880.730-0.324-0.0100.8591.1221.009-0.3490.539-0.5540.00.06.667-0.583FalseTrueFalseFalse1
71.2051.20-0.151-1.254FalseTrueFalseFalse-0.080-0.0951.457-0.502-0.382-0.699-0.6360.1161.7532.2320.00.01.3330.333FalseTrueFalseFalse1
81.0001.20-0.747-0.923FalseFalseFalseFalse-0.096-0.159-0.130-0.739-0.350-0.569-0.523-0.722-0.0450.4110.00.01.3331.333FalseTrueFalseFalse1
90.6461.60-0.958-1.041FalseFalseFalseTrue-0.047-0.159-0.049-0.049-0.382-0.3410.224-0.722-0.0450.0180.00.01.3330.750FalseTrueFalseFalse1
tem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntucr_total_mg_lclorofila_a_ug_lmicrocistina_ug_licacampaña_Primaveracampaña_Veranocampaña_inviernocampaña_otoñoCluster
158-0.102-1.00.6260.414FalseFalseFalseFalse-0.103-0.159-0.4370.108-0.3240.6990.187-0.5171.8880.4119.9951.5080.000-0.083TrueFalseFalseFalse1
159-0.378-1.61.4151.929FalseFalseFalseFalse-0.102-0.171-0.3400.049-0.318-0.081-0.449-0.5170.1350.1250.0000.6221.067-0.333TrueFalseFalseFalse1
160-0.155-1.80.5350.651FalseFalseFalseFalse-0.105-0.200-0.4050.010-0.153-0.309-0.374-0.5170.719-0.0540.0000.4560.000-0.083TrueFalseFalseFalse1
161-0.006-2.20.1990.391FalseFalseFalseFalse-0.0850.222-0.130-0.0491.049-0.081-0.374-0.2330.000-0.1610.0000.1570.000-0.417TrueFalseFalseFalse1
162-0.173-2.00.7330.604FalseFalseFalseFalse-0.104-0.203-0.4130.089-0.159-0.472-0.336-0.5170.0000.4110.000-0.2210.0001.000TrueFalseFalseFalse1
163-1.496-0.60.0120.374FalseFalseFalseTrue-0.0850.1590.1620.246-0.172-0.472-0.336-0.2330.000-0.1610.000-0.1860.0000.083TrueFalseFalseFalse1
164-1.496-0.6-0.831-0.149FalseTrueFalseTrue2.09650.57118.947-0.0890.35096.6990.224-0.2560.045-0.3320.0000.2400.000-0.417TrueFalseFalseFalse0
165-1.496-0.6-0.050-0.073FalseFalseFalseTrue-0.0690.4130.130-0.2070.0950.2110.3740.5260.0000.4460.000-0.2300.0000.583TrueFalseFalseFalse1
166-1.496-0.60.336-0.095FalseFalseFalseTrue-0.0580.7300.324-0.089-0.064-0.374-0.2240.4140.4042.2324.9950.0120.000-0.250TrueFalseFalseFalse1
167-1.496-0.60.2250.367FalseFalseFalseTrue-0.0830.1590.291-0.108-0.2800.3090.075-0.0560.0001.5184.9950.8930.000-0.667TrueFalseFalseFalse1